Faults Identification of Induction Motor Based On Vibration Using Backpropagation Neural Network

Kuspijani, Kuspijani; Watiasih, Richa; Prihastono, Prihastono

doi:10.1109/icosta48221.2020.1570615779

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…The researchers effectively demonstrated the usage of predictive maintenance in their study. Along with the Backpropagation Neural Network models discussed in 2020, 20 advanced neural network methodologies such as the Convolutional Neural Network (CNN) have also been implemented for Assets Predictive Maintenance. 21 Some of the listed references related to the architecture of smart and predictive maintenance systems are well aligned with IR 4.0 principles.…”

Section: Various Predictive Maintenance Techniquesmentioning

confidence: 99%

A heuristic approach on predictive maintenance techniques: Limitations and scope

Shukla

Nefti‐Meziani

Davis

2022

Advances in Mechanical Engineering

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In view of the trend towards Industry 4.0, intelligent predictive monitoring and decision-making processes have become a crucial requirement in today’s manufacturing industries to safeguard data exchange and industrial assets from damage that would thus prevent the achievement of overall company goals. For enhanced reliability and safe operation of machines, frequent maintenance of the process equipment and the linked auxiliaries in a plant is highly desirable. Poor maintenance of assets can add to downtime, which can in turn affect the overall cost-effectiveness of the plant. With traditional maintenance strategies and planned or timed-based maintenance, one replaces the faulty systems when they are found to be damaged or broken. However, an early and proactive prediction of machine or equipment fault and failure state enables the industry to take the necessary action to replace the faulty system well before it stops operating entirely. This paper briefly reviews the available predictive maintenance techniques for different applications from the perspective of Industry 4.0. Furthermore, the associated challenges and opportunities are identified and discussed.

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Section: Various Predictive Maintenance Techniquesmentioning

confidence: 99%

A heuristic approach on predictive maintenance techniques: Limitations and scope

Shukla

Nefti‐Meziani

Davis

2022

Advances in Mechanical Engineering

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“…The time-frequency analysis methods combined the two to form a joint function, could describe the non-linear and non-stationary dynamic signals of complex mechanical equipment, such as [21,22]. Recently, with the development of machine learning and deep learning, the accuracy of fault diagnosis of rolling bearings obtained great improvement, such as support vector machine based methods [23,24], Bayesian classifier [25], and neural network based algorithms [26][27][28][29][30][31]. More details are described in Section 2.…”

Section: Existing Situationmentioning

confidence: 99%

“…The third type pertains to neural-network-based algorithms, including the BP neural network [34], convolutional neural network [26,35], and extreme learning machine [27]. For example, the fault diagnosis algorithm proposed by [28] in 2020 involved 2000 input neurons and a hidden layer containing 10 neurons. Reference [29] calculated the time-frequency features of vibration signals by realizing the continuous wavelet transform (CWT) of the complex Morlet wavelet and performing jointed time-frequency analysis (JTFA).…”

Section: Related Workmentioning

confidence: 99%

Logistic-ELM: A Novel Fault Diagnosis Method for Rolling Bearings

Tan¹,

Ning²,

Peng³

et al. 2022

Preprint

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Logistic-ELMcost, the experimental results indicate that the proposed logistic-ELM can predict the fault in 40 ms with a high accuracy, up to 21-1858 times more rapidly than existing methods based on SVM, CNN and multiscale entropy. Other experiments of fault diagnosis of the rolling bearings under four different loads, also indicate that the logistic-ELM can adapt to different operation conditions with high efficiency. The relevant code is publicly available at https://github.com/TAN-OpenLab/logistic-ELM.

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“…Many studies have confirmed the role of vibration analysis in fault detection of induction motor. A vibration detection system is designed by Kuspijani et al [12] that can be used for online monitoring of an induction motor. Induction motor vibration data is read from two vibration sensors and the results are analyzed by using artificial intelligence-based analysis.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Vibration as a Useful Domain for Getting Bearing Fault Signals in Induction Motors

Tabasi

Ojaghi

Mostafavi

2021

IJE

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Faults Identification of Induction Motor Based On Vibration Using Backpropagation Neural Network

Cited by 4 publications

References 3 publications

A heuristic approach on predictive maintenance techniques: Limitations and scope

A heuristic approach on predictive maintenance techniques: Limitations and scope

Logistic-ELM: A Novel Fault Diagnosis Method for Rolling Bearings

Analyzing Vibration as a Useful Domain for Getting Bearing Fault Signals in Induction Motors

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